Oil and gas prospect evaluation and field development require accurate characterization of subsurface features. Seismic acquisition over subsurface structure generally produces time-domain data, which is then migrated to, for example, depth image data. The migration process necessarily involves certain assumptions regarding the propagation velocity of elastic waves through the subsurface materials and structures. Moreover, there is generally some degree of anisotropy in geological formations. That is, while it may be possible to determine vertical velocities using well data, the velocities estimated using multi-offset seismic techniques will necessarily be somewhat different from measured vertical velocities. Finally, because assumptions, based on measurement or estimation, regarding both velocities and degrees of anisotropy may be incorrect, there is some inherent uncertainty in the resulting depth image, both in the depth of imaged events and in the structural interpretation of the events.
Models of the subsurface obtained from geophysical measurements are inherently non-unique. Geophysical measurements are finite in resolution, and relate to many orders of magnitude of scale. Uncertainty in the measurements results from a variety of sources, including signal-to-noise ratio, data acquisition parameter selection, processing algorithms, or the above mentioned velocity and anisotropy parameter selection. It is therefore important to understand the degree of that uncertainty when evaluating model results. That is, it is important to quantitatively understand to what degree the models are sensitive to a given change or group of changes in the assumptions regarding velocities, anisotropy or the other factors impacting uncertainty. An understanding of the uncertainty and the range of possible characterizations allows interpreters of the data to make business decisions regarding reserve estimation, well placement and count, development scenarios, secondary recovery strategies and other factors that ultimately impact recovery and project economics.